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<div class="title">tools/util/include/cutlass/util/reference/host/gemm.h</div>  </div>
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<a href="tools_2util_2include_2cutlass_2util_2reference_2host_2gemm_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/***************************************************************************************************</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017-2019, NVIDIA CORPORATION.  All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * Redistribution and use in source and binary forms, with or without modification, are permitted</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * provided that the following conditions are met:</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *     * Redistributions of source code must retain the above copyright notice, this list of</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *       conditions and the following disclaimer.</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *     * Redistributions in binary form must reproduce the above copyright notice, this list of</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *       conditions and the following disclaimer in the documentation and/or other materials</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *       provided with the distribution.</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *       to endorse or promote products derived from this software without specific prior written</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *       permission.</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS &quot;AS IS&quot; AND ANY EXPRESS OR</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment"> **************************************************************************************************/</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="coord_8h.html">cutlass/coord.h</a>&quot;</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="numeric__types_8h.html">cutlass/numeric_types.h</a>&quot;</span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="functional_8h.html">cutlass/functional.h</a>&quot;</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="numeric__conversion_8h.html">cutlass/numeric_conversion.h</a>&quot;</span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="matrix__traits_8h.html">cutlass/matrix_traits.h</a>&quot;</span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tensor__view_8h.html">cutlass/tensor_view.h</a>&quot;</span></div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="include_2cutlass_2gemm_2gemm_8h.html">cutlass/gemm/gemm.h</a>&quot;</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="arch_2mma_8h.html">cutlass/arch/mma.h</a>&quot;</span></div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacecutlass.html">cutlass</a> {</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="keyword">namespace </span>reference {</div><div class="line"><a name="l00043"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1host.html">   43</a></span>&#160;<span class="keyword">namespace </span>host {</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;  <span class="keyword">typename</span> ElementA,</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  <span class="keyword">typename</span> LayoutA,</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  <span class="keyword">typename</span> ElementB,</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  <span class="keyword">typename</span> LayoutB,</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  <span class="keyword">typename</span> ElementC,</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  <span class="keyword">typename</span> LayoutC,</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;  <span class="keyword">typename</span> ScalarType,</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  <span class="keyword">typename</span> ComputeType,</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;  <span class="keyword">typename</span> InnerProductOp = <a class="code" href="structcutlass_1_1multiply__add.html">multiply_add&lt;ComputeType&gt;</a>,</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <span class="keyword">typename</span> ConvertOp = <a class="code" href="structcutlass_1_1NumericConverter.html">NumericConverter&lt;ElementC, ScalarType&gt;</a></div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;&gt;</div><div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">   61</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>(</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html">gemm::GemmCoord</a> problem_size,</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  ScalarType alpha,</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementA, LayoutA&gt;</a> tensor_a,</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementB, LayoutB&gt;</a> tensor_b,</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;  ScalarType beta,</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_c,</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_d,</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  ComputeType initial_accum) {</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;  <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;    LayoutA::kRank == 2 &amp;&amp;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    LayoutB::kRank == 2 &amp;&amp;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    LayoutC::kRank == 2, <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  <span class="comment">// Note: batch is ignored.</span></div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  <span class="keywordtype">int</span> <span class="keyword">const</span> M = problem_size.<a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html#a93515a41db6c4b7e9101067f60d41b8c">m</a>();</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  <span class="keywordtype">int</span> <span class="keyword">const</span> N = problem_size.<a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html#a1b29d2cb15360ad5499216859ad5436a">n</a>();</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  <span class="keywordtype">int</span> <span class="keyword">const</span> K = problem_size.<a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html#a18835ec84cbb6250143327e93697c7e9">k</a>();</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;  <span class="comment">// Blocking necessary to speedup reference implementation</span></div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  <span class="keywordtype">int</span> <span class="keyword">const</span> Mblock = 16;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  <span class="keywordtype">int</span> <span class="keyword">const</span> Nblock = 16;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  ConvertOp convert_op;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;  InnerProductOp inner_product_op;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row_block = 0; row_block &lt; M; row_block += Mblock) {</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col_block = 0; col_block &lt; N; col_block += Nblock) {</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;      ComputeType accum[Mblock][Nblock];</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; Nblock; j++) {</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; Mblock; i++) {</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;          accum[i][j] = initial_accum;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        }</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;      }</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k_block = 0; k_block &lt; K; ++k_block) {</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; Nblock; j++) {</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; Mblock; i++) {</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;            <span class="keywordtype">int</span> row = row_block + i;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;            <span class="keywordtype">int</span> col = col_block + j;</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;            <span class="keywordflow">if</span> (row &lt; M &amp;&amp; col &lt; N) {</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;              ElementA a = tensor_a.<a class="code" href="classcutlass_1_1TensorRef.html#a8758907a1c9b1fcd00e7ece626d03b76">at</a>(<a class="code" href="structcutlass_1_1MatrixCoord.html">MatrixCoord</a>(row, k_block));</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160; 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 <span class="keyword">typename</span> ElementC,</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  <span class="keyword">typename</span> LayoutC,</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  <span class="keyword">typename</span> ScalarType,</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  <span class="keyword">typename</span> ComputeType,</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;  <span class="keyword">typename</span> InnerProductOp = <a class="code" href="structcutlass_1_1multiply__add.html">multiply_add&lt;ComputeType&gt;</a>,</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;  <span class="keyword">typename</span> ConvertOp = <a class="code" href="structcutlass_1_1NumericConverter.html">NumericConverter&lt;ElementC, ScalarType&gt;</a></div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;&gt;</div><div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1host.html#aa75c5933390f3960666e97b37c854877">  150</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>(</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160; 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                 ComputeType initial_accum = ComputeType(0)) {</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        LayoutA::kRank == 2 &amp;&amp; LayoutB::kRank == 2 &amp;&amp; LayoutC::kRank == 2,</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160; 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       LayoutA::kRank == 2 &amp;&amp; LayoutB::kRank == 2 &amp;&amp; LayoutC::kRank == 2,</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;                 ScalarType, ComputeType, <a class="code" href="structcutlass_1_1multiply__add.html">multiply_add&lt;ComputeType&gt;</a>&gt;(</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;        problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, tensor_d, initial_accum);</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160; 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                 <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementB, LayoutB&gt;</a> tensor_b, ScalarType beta,</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_c,</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;                  ComputeType initial_accum = ComputeType(0)) {</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;        LayoutA::kRank == 2 &amp;&amp; LayoutB::kRank == 2 &amp;&amp; LayoutC::kRank == 2,</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;        <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;                 ScalarType, ComputeType, multiply_add&lt;ComputeType&gt;,</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;                 <a class="code" href="structcutlass_1_1NumericConverterClamp.html">NumericConverterClamp&lt;ElementC, ScalarType&gt;</a>&gt;(</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, initial_accum);</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;  }</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93.html#ae177833020d1dce029276863a5d77222">  242</a></span>&#160;  <span class="keywordtype">void</span> <a class="code" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93.html#ae177833020d1dce029276863a5d77222">operator()</a>(<a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html">gemm::GemmCoord</a> problem_size, ScalarType alpha,</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementA, LayoutA&gt;</a> tensor_a,</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementB, LayoutB&gt;</a> tensor_b, ScalarType beta,</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_c,</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_d,</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;                  ComputeType initial_accum = ComputeType(0)) {</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        LayoutA::kRank == 2 &amp;&amp; LayoutB::kRank == 2 &amp;&amp; LayoutC::kRank == 2,</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;                 ScalarType, ComputeType, multiply_add&lt;ComputeType&gt;,</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;                 <a class="code" href="structcutlass_1_1NumericConverterClamp.html">NumericConverterClamp&lt;ElementC, ScalarType&gt;</a>&gt;(</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, tensor_d, initial_accum);</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;  }</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;};</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> ElementA, <span class="keyword">typename</span> LayoutA, <span class="keyword">typename</span> ElementB,</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;          <span class="keyword">typename</span> LayoutB, <span class="keyword">typename</span> ElementC, <span class="keyword">typename</span> LayoutC,</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;          <span class="keyword">typename</span> ScalarType, <span class="keyword">typename</span> ComputeType&gt;</div><div class="line"><a name="l00265"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html">  265</a></span>&#160;<span class="keyword">struct </span><a class="code" href="structcutlass_1_1reference_1_1host_1_1Gemm.html">Gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType,</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;            ComputeType, arch::OpXorPopc&gt; {</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html#acb564b7ad68fa082a6c785e919a9de6a">  268</a></span>&#160;  <span class="keywordtype">void</span> <a class="code" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html#acb564b7ad68fa082a6c785e919a9de6a">operator()</a>(<a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html">gemm::GemmCoord</a> problem_size, ScalarType alpha,</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementA, LayoutA&gt;</a> tensor_a,</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementB, LayoutB&gt;</a> tensor_b, ScalarType beta,</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_c,</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;                  ComputeType initial_accum = ComputeType(0)) {</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;    <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        LayoutA::kRank == 2 &amp;&amp; LayoutB::kRank == 2 &amp;&amp; LayoutC::kRank == 2,</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;                 ScalarType, ComputeType, <a class="code" href="structcutlass_1_1xor__add.html">xor_add&lt;ComputeType&gt;</a>&gt;(</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, initial_accum);</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;  }</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;</div><div class="line"><a name="l00282"></a><span class="lineno"><a class="line" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html#a3721982c8e5afa9842f4fba1e9c23909">  282</a></span>&#160;  <span class="keywordtype">void</span> <a class="code" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html#a3721982c8e5afa9842f4fba1e9c23909">operator()</a>(<a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html">gemm::GemmCoord</a> problem_size, ScalarType alpha,</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementA, LayoutA&gt;</a> tensor_a,</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementB, LayoutB&gt;</a> tensor_b, ScalarType beta,</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_c,</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;                  <a class="code" href="classcutlass_1_1TensorRef.html">TensorRef&lt;ElementC, LayoutC&gt;</a> tensor_d,</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;                  ComputeType initial_accum = ComputeType(0)) {</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    <a class="code" href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a>(</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;        LayoutA::kRank == 2 &amp;&amp; LayoutB::kRank == 2 &amp;&amp; LayoutC::kRank == 2,</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;        <span class="stringliteral">&quot;Tensors must be of rank 2&quot;</span>);</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">compute_gemm</a>&lt;ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC,</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;                 ScalarType, ComputeType, <a class="code" href="structcutlass_1_1xor__add.html">xor_add&lt;ComputeType&gt;</a>&gt;(</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        problem_size, alpha, tensor_a, tensor_b, beta, tensor_c, tensor_d, initial_accum);</div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;  }</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;};</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;<span class="comment">// Batched GEMM</span></div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;<span class="comment">// TensorRefCollection* is a type satisfying the TensorRefCollection concept.</span></div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionA,</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionB,</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionC,</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;  <span class="keyword">typename</span> ScalarType,</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;  <span class="keyword">typename</span> AccumulatorType</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;&gt;</div><div class="line"><a name="l00315"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1host.html#a2c1067fa5de91e2f48589120f62125c2">  315</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a2c1067fa5de91e2f48589120f62125c2">BatchedGemm</a>(</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;  <a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html">gemm::GemmCoord</a> problem_size,</div><div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;  <span class="keywordtype">int</span> batch_count,</div><div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;  ScalarType alpha,</div><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;  TensorRefCollectionA <span class="keyword">const</span>&amp; tensor_a,</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;  TensorRefCollectionB <span class="keyword">const</span>&amp; tensor_b,</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;  ScalarType beta,</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;  TensorRefCollectionC &amp;tensor_c,</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  AccumulatorType initial_accum) {</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionA::ConstIterator tensor_a_it = tensor_a.begin();</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionB::ConstIterator tensor_b_it = tensor_b.begin();</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionC::ConstIterator tensor_c_it = tensor_c.begin();</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> batch = 0;</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    batch &lt; batch_count;</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;    ++batch, ++tensor_a_it, ++tensor_b_it, ++tensor_c_it) {</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    </div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <a class="code" href="structcutlass_1_1reference_1_1host_1_1Gemm.html">Gemm</a>&lt;<span class="keyword">typename</span> TensorRefCollectionA::Element,</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionA::Layout,</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionB::Element,</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionB::Layout,</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionC::Element,</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionC::Layout,</div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionC::Element,</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;         <span class="keyword">typename</span> TensorRefCollectionC::Element&gt;</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        gemm;</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    gemm(problem_size, alpha, *tensor_a_it, *tensor_b_it, beta, *tensor_c_it,</div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;         initial_accum);</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;  }</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;}</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;<span class="comment">// TensorRefCollection* is a type satisfying the TensorRefCollection concept.</span></div><div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;<span class="comment">//</span></div><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;<span class="keyword">template</span> &lt;</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionA,</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionB,</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  <span class="keyword">typename</span> TensorRefCollectionC,</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;  <span class="keyword">typename</span> ScalarType,</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;  <span class="keyword">typename</span> AccumulatorType</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;&gt;</div><div class="line"><a name="l00360"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reference_1_1host.html#a1d0a79a48353119706ffa09d570c2182">  360</a></span>&#160;<span class="keywordtype">void</span> <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a2c1067fa5de91e2f48589120f62125c2">BatchedGemm</a>(</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;  <a class="code" href="structcutlass_1_1gemm_1_1GemmCoord.html">gemm::GemmCoord</a> problem_size,</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;  <span class="keywordtype">int</span> batch_count,</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;  ScalarType alpha,</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  TensorRefCollectionA <span class="keyword">const</span>&amp; tensor_a,</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;  TensorRefCollectionB <span class="keyword">const</span>&amp; tensor_b,</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  ScalarType beta,</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;  TensorRefCollectionC &amp;tensor_c) {</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  <a class="code" href="namespacecutlass_1_1reference_1_1host.html#a2c1067fa5de91e2f48589120f62125c2">BatchedGemm</a>(problem_size, batch_count, alpha, tensor_a, tensor_b, beta, tensor_c, ScalarType(0));</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;}</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;} <span class="comment">// namespace host</span></div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;} <span class="comment">// namespace reference</span></div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;} <span class="comment">// namespace cutlass</span></div><div class="ttc" id="structcutlass_1_1multiply__add_html"><div class="ttname"><a href="structcutlass_1_1multiply__add.html">cutlass::multiply_add</a></div><div class="ttdoc">Fused multiply-add. </div><div class="ttdef"><b>Definition:</b> functional.h:92</div></div>
<div class="ttc" id="namespacecutlass_1_1reference_1_1host_html_a2c1067fa5de91e2f48589120f62125c2"><div class="ttname"><a href="namespacecutlass_1_1reference_1_1host.html#a2c1067fa5de91e2f48589120f62125c2">cutlass::reference::host::BatchedGemm</a></div><div class="ttdeci">void BatchedGemm(gemm::GemmCoord problem_size, int batch_count, ScalarType alpha, TensorRefCollectionA const &amp;tensor_a, TensorRefCollectionB const &amp;tensor_b, ScalarType beta, TensorRefCollectionC &amp;tensor_c, AccumulatorType initial_accum)</div><div class="ttdoc">Computes a batch of GEMMs over a set of matrices of common dimension. </div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:315</div></div>
<div class="ttc" id="namespacecutlass_html"><div class="ttname"><a href="namespacecutlass.html">cutlass</a></div><div class="ttdef"><b>Definition:</b> aligned_buffer.h:35</div></div>
<div class="ttc" id="structcutlass_1_1NumericConverterClamp_html"><div class="ttname"><a href="structcutlass_1_1NumericConverterClamp.html">cutlass::NumericConverterClamp</a></div><div class="ttdef"><b>Definition:</b> numeric_conversion.h:254</div></div>
<div class="ttc" id="coord_8h_html"><div class="ttname"><a href="coord_8h.html">coord.h</a></div><div class="ttdoc">A Coord is a coordinate of arbitrary rank into a tensor or matrix. </div></div>
<div class="ttc" id="structcutlass_1_1gemm_1_1GemmCoord_html"><div class="ttname"><a href="structcutlass_1_1gemm_1_1GemmCoord.html">cutlass::gemm::GemmCoord</a></div><div class="ttdef"><b>Definition:</b> include/cutlass/gemm/gemm.h:94</div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_html"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm.html">cutlass::reference::host::Gemm</a></div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:177</div></div>
<div class="ttc" id="include_2cutlass_2gemm_2gemm_8h_html"><div class="ttname"><a href="include_2cutlass_2gemm_2gemm_8h.html">gemm.h</a></div><div class="ttdoc">Defines common types used for all GEMM-like operators. </div></div>
<div class="ttc" id="structcutlass_1_1gemm_1_1GemmCoord_html_a1b29d2cb15360ad5499216859ad5436a"><div class="ttname"><a href="structcutlass_1_1gemm_1_1GemmCoord.html#a1b29d2cb15360ad5499216859ad5436a">cutlass::gemm::GemmCoord::n</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Index const &amp; n() const </div><div class="ttdoc">Returns the GEMM N coordinate. </div><div class="ttdef"><b>Definition:</b> include/cutlass/gemm/gemm.h:137</div></div>
<div class="ttc" id="tensor__view_8h_html"><div class="ttname"><a href="tensor__view_8h.html">tensor_view.h</a></div><div class="ttdoc">Defines a structure containing strides and a pointer to tensor data. </div></div>
<div class="ttc" id="structcutlass_1_1gemm_1_1GemmCoord_html_a18835ec84cbb6250143327e93697c7e9"><div class="ttname"><a href="structcutlass_1_1gemm_1_1GemmCoord.html#a18835ec84cbb6250143327e93697c7e9">cutlass::gemm::GemmCoord::k</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Index const &amp; k() const </div><div class="ttdoc">Returns the GEMM K coordinate. </div><div class="ttdef"><b>Definition:</b> include/cutlass/gemm/gemm.h:145</div></div>
<div class="ttc" id="arch_2mma_8h_html"><div class="ttname"><a href="arch_2mma_8h.html">mma.h</a></div><div class="ttdoc">Templates exposing architecture support for multiply-add operations. </div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93_html_ae177833020d1dce029276863a5d77222"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93.html#ae177833020d1dce029276863a5d77222">cutlass::reference::host::Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpMultiplyAddSaturate &gt;::operator()</a></div><div class="ttdeci">void operator()(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, TensorRef&lt; ElementC, LayoutC &gt; tensor_d, ComputeType initial_accum=ComputeType(0))</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:242</div></div>
<div class="ttc" id="numeric__conversion_8h_html"><div class="ttname"><a href="numeric__conversion_8h.html">numeric_conversion.h</a></div><div class="ttdoc">Boost-like numeric conversion operator for CUTLASS numeric types. </div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html"><div class="ttname"><a href="classcutlass_1_1TensorRef.html">cutlass::TensorRef&lt; ElementA, LayoutA &gt;</a></div></div>
<div class="ttc" id="numeric__types_8h_html"><div class="ttname"><a href="numeric__types_8h.html">numeric_types.h</a></div><div class="ttdoc">Top-level include for all CUTLASS numeric types. </div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_193dd3a37f00deff1e5dcd7c310afb1f_html_abf6e1517db61bb6e4624e91e083f5956"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_193dd3a37f00deff1e5dcd7c310afb1f.html#abf6e1517db61bb6e4624e91e083f5956">cutlass::reference::host::Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpMultiplyAdd &gt;::operator()</a></div><div class="ttdeci">void operator()(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, TensorRef&lt; ElementC, LayoutC &gt; tensor_d, ComputeType initial_accum=ComputeType(0))</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:202</div></div>
<div class="ttc" id="platform_8h_html_adde4c9ea91b753491851361a4198c009"><div class="ttname"><a href="platform_8h.html#adde4c9ea91b753491851361a4198c009">static_assert</a></div><div class="ttdeci">#define static_assert(__e, __m)</div><div class="ttdef"><b>Definition:</b> platform.h:153</div></div>
<div class="ttc" id="structcutlass_1_1NumericConverter_html"><div class="ttname"><a href="structcutlass_1_1NumericConverter.html">cutlass::NumericConverter</a></div><div class="ttdef"><b>Definition:</b> numeric_conversion.h:59</div></div>
<div class="ttc" id="classcutlass_1_1TensorRef_html_a8758907a1c9b1fcd00e7ece626d03b76"><div class="ttname"><a href="classcutlass_1_1TensorRef.html#a8758907a1c9b1fcd00e7ece626d03b76">cutlass::TensorRef::at</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Reference at(TensorCoord const &amp;coord) const </div><div class="ttdoc">Returns a reference to the element at a given Coord. </div><div class="ttdef"><b>Definition:</b> tensor_ref.h:307</div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46_html_a3721982c8e5afa9842f4fba1e9c23909"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html#a3721982c8e5afa9842f4fba1e9c23909">cutlass::reference::host::Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpXorPopc &gt;::operator()</a></div><div class="ttdeci">void operator()(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, TensorRef&lt; ElementC, LayoutC &gt; tensor_d, ComputeType initial_accum=ComputeType(0))</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:282</div></div>
<div class="ttc" id="namespacecutlass_1_1reference_1_1host_html_a300d68abd082150020768c0a94044a34"><div class="ttname"><a href="namespacecutlass_1_1reference_1_1host.html#a300d68abd082150020768c0a94044a34">cutlass::reference::host::compute_gemm</a></div><div class="ttdeci">void compute_gemm(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, TensorRef&lt; ElementC, LayoutC &gt; tensor_d, ComputeType initial_accum)</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:61</div></div>
<div class="ttc" id="structcutlass_1_1xor__add_html"><div class="ttname"><a href="structcutlass_1_1xor__add.html">cutlass::xor_add</a></div><div class="ttdoc">Fused multiply-add. </div><div class="ttdef"><b>Definition:</b> functional.h:101</div></div>
<div class="ttc" id="structcutlass_1_1gemm_1_1GemmCoord_html_a93515a41db6c4b7e9101067f60d41b8c"><div class="ttname"><a href="structcutlass_1_1gemm_1_1GemmCoord.html#a93515a41db6c4b7e9101067f60d41b8c">cutlass::gemm::GemmCoord::m</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Index const &amp; m() const </div><div class="ttdoc">Returns the GEMM M coordinate. </div><div class="ttdef"><b>Definition:</b> include/cutlass/gemm/gemm.h:129</div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_193dd3a37f00deff1e5dcd7c310afb1f_html_ab1279c5fa79550cdd993ce5241eaac24"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_193dd3a37f00deff1e5dcd7c310afb1f.html#ab1279c5fa79550cdd993ce5241eaac24">cutlass::reference::host::Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpMultiplyAdd &gt;::operator()</a></div><div class="ttdeci">void operator()(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, ComputeType initial_accum=ComputeType(0))</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:188</div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93_html_ac41fc498b787cd86d4433608121caffc"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93.html#ac41fc498b787cd86d4433608121caffc">cutlass::reference::host::Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpMultiplyAddSaturate &gt;::operator()</a></div><div class="ttdeci">void operator()(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, ComputeType initial_accum=ComputeType(0))</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:227</div></div>
<div class="ttc" id="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46_html_acb564b7ad68fa082a6c785e919a9de6a"><div class="ttname"><a href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html#acb564b7ad68fa082a6c785e919a9de6a">cutlass::reference::host::Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpXorPopc &gt;::operator()</a></div><div class="ttdeci">void operator()(gemm::GemmCoord problem_size, ScalarType alpha, TensorRef&lt; ElementA, LayoutA &gt; tensor_a, TensorRef&lt; ElementB, LayoutB &gt; tensor_b, ScalarType beta, TensorRef&lt; ElementC, LayoutC &gt; tensor_c, ComputeType initial_accum=ComputeType(0))</div><div class="ttdef"><b>Definition:</b> tools/util/include/cutlass/util/reference/host/gemm.h:268</div></div>
<div class="ttc" id="matrix__traits_8h_html"><div class="ttname"><a href="matrix__traits_8h.html">matrix_traits.h</a></div><div class="ttdoc">Defines properties of matrices used to denote layout and operands to GEMM kernels. </div></div>
<div class="ttc" id="structcutlass_1_1MatrixCoord_html"><div class="ttname"><a href="structcutlass_1_1MatrixCoord.html">cutlass::MatrixCoord</a></div><div class="ttdef"><b>Definition:</b> matrix_coord.h:39</div></div>
<div class="ttc" id="functional_8h_html"><div class="ttname"><a href="functional_8h.html">functional.h</a></div><div class="ttdoc">Define basic numeric operators with specializations for Array&lt;T, N&gt;. SIMD-ize where possible...</div></div>
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